The Transformer Architecture Playbook
A practical deep-dive into transformer architecture from attention mechanics to production-scale optimization.
W1
Foundations: Attention Mechanics & QKV Dynamics
By the end of this module you will be able to mathematically derive and implement scaled dot-product attention with proper gradient flow.
4 videos•123m
3 readings
4 topics
1 homework
Topics
1.1
Scaled Dot Product Attention | Why do we scale Self Attention?
Scaled Dot-Product Attention: Mathematical Formulation
50 minutes
1.2
What are Transformer Neural Networks?
Gradient Flow Through Attention Layers
16 minutes
1.3
Visual Guide to Transformer Neural Networks - (Episode 2) Multi-Head & Self-Attention
Multi-Head Attention: Split-Transform-Merge Design
15 minutes
1.4
The Transformer Model EXPLAINED: Math, Attention & Code. The Only Guide You Need!
Positional Encoding: Injecting Sequence Order
42 minutes
References
Week 1: Foundations: Attention Mechanics & QKV Dynamics
Week 2: Core Architecture: Encoder & Decoder Stacks
Week 3: Efficiency: Sparse & Memory-Attention Variants
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